Developing a Digital Twin Model for Improved Pasture Management at Sheep Farm to Mitigate the Impact of Climate Change

Small-scale livestock farmers experience significant losses because of decreased productivity caused by decline in pasture production brought on by climate change. Technology in livestock farming introduced the idea of "smart farming," which has simplified pasture management. Internet of Things (IoT), Artificial Intelligence (AI) and data analytics are just a few of the cutting-edge technology techniques that smart farming incorporates. Digital twin technology is proposed in this study to alleviate the challenge of changing weather patterns that affect pasture management. Digital twin model is developed to predict pasture height to ascertain the predicted amount of pasture and ensure that the sheep have access to enough food for sustainable production. Pasture growth is influenced by temperature, rainfall and soil moisture; thus, pasture height predictions depend on these factors. Digital twin is made of predictive models built on historical and real-time data collected from the IoT sensors and stored in ThingSpeak® cloud. Data analysis was performed in MATLAB® using the neural network algorithm and predictions of the system are modelled in SIMULINK® platform. Digital twin predicted the pasture height to be 52 cm while the observed reading was 56 cm. Therefore, with the prediction error of -4, the digital twin can serve to enhance pasture management through its capabilities and assist farmers in decision making.

@article{Lemphane2024,
  title     = {Developing a Digital Twin Model for Improved Pasture Management at Sheep Farm to Mitigate the Impact of Climate Change},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ntebaleng Junia Lemphane and Ben Kotze and Rangith Baby Kuriakose},
  doi       = {10.14569/IJACSA.2024.0150627},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150627}
}